Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Simulation of power plants steam generators and cooling towers with artificial neural network

A modelagem da operação de equipamentos é uma opção metodológica importante para a melhoria da eficiência de usinas geradoras de energia. Uma dessas metodologias é a rede neural artificial (RNA), que vem ganhando espaço devido à sua capacidade de modelar problemas complexos com base em comportamentos registrados de sistemas reais. O objetivo do presente estudo é desenvolver modelos de RNA capazes de reproduzir o funcionamento do gerador de vapor e da torre úmida de arrefecimento da planta termoelétrica a carvão de PECÉM, no estado do Ceará, Brasil. O modelo de RNA para o gerador de vapor superaquecido a carvão estima a vazão mássica de vapor com base em registros de um ano de operação da Usina. A configuração das RNAs é obtida após uma série de testes com o objetivo de reduzir o erro de predição através do erro absoluto médio (EAM) em diferentes patamares de operação, obtendo-se um MAE de 1,28% para o conjunto total de dados de operação, 8,11% para a faixa de operação de 240 MW e 10,82% para a faixa de operação de 360 MW. O desempenho das redes é comparado ao de modelos de regressão linear múltipla aplicados ao mesmo conjunto de dados, para os quais se têm valores de MAE de 2,05%, 9,47% e 15,76%. Esses resultados mostram a capacidade da RNA de estimar a produção de vapor com erro abaixo daqueles de modelos de regressão. O modelo de RNA é desenvolvido para um dos conjuntos de torres úmidas de resfriamento ligado ao sistema de condensação de uma das plantas do sitio de geração. Essa planta é referenciada como de melhor desempenho e o modelo RNA gerado é aplicado aos dados de operação do segundo conjunto de torres, ajudando na identificação de possíveis desvios ou problemas de desempenho. Ferramentas estatísticas são usadas para avaliar os dois conjuntos de dados referentes as torres de cada usina e identificar correlações de parâmetros. Os modelos de RNA com melhor desempenho são obtidos com um coeficiente máximo de correlação R² de 0,9956 para a taxa de calor rejeitada e 0,8699 para a taxa de vazão mássica de água de reposição para o conjunto de dados de referência. O coeficiente R² encontrado para o segundo conjunto de torres é de 0,748 para a taxa de calor rejeitada e 0,905 para a vazão mássica de água de reposição. Esse resultado ajuda a identificar alguns comportamentos não padronizados da torre. Uma nova simulação sem os pontos de fora da curva (outlier) exibiu valores de R² de 0,98 e 0,99, respectivamente.The modeling of equipment operation is an important methodological option for improving the efficiency of power plants. One of these methodologies is the artificial neural network (ANN), which is gaining space due to its ability to model complex problems based on acquired data from real systems. The objective of the present study is to develop ANN models capable of reproducing the operation of the steam generator and the wet cooling tower of the PECÉM coal-fired power plant in the state of Ceara, Brazil. The ANN model for the coal superheated steam generator estimates the steam mass flow rate based on year-long records of operation. ANN configuration is obtained after a series of tests with the objective of reducing the ANN mean absolute error (MAE) in different levels of operation, obtaining an MAE of 1,28% for the total set of data of operation, 8.11% for the 240 MW operating range and 10.82% for the 360 MW operating range. The network performance is compared to that of multiple linear regression models applied to the same data set, with MAE values of 2.05%, 9.47% and 15.76%. These results show the ability of ANN to estimate the production of vapor with errors below those of regression models. The ANN model is developed for one set of wet cooling towers connected to the condensation system. This plant is referred to present the best performance and the generated ANN model is applied to the operation data of the second plant, helping to identify possible deviations or performance problems. Statistical tools are used to evaluate the two cooling towers and to identify parameter correlations. The best performing ANN models are obtained with a R² correlation coefficient of 0.9956 for the rejected heat rate and 0.8699 for the makeup water mass flow rate for the reference data set. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. This result helps to identify some non-standard behavior of the tower. A new simulation without the outlier points exhibited R² values of 0.98 and 0.99, respectively

NASA Tech Briefs, June 2001

Topics covered include: Sensors; Electronic Components and Systems; Software Engineering; Materials; Manufacturing/Fabrication; physical Sciences; Information Sciences

Dynamic Modeling and Wavelet-Based Multi-Parametric Tuning and Validation for HVAC Systems

Dynamic Heating, Ventilation, and Air-Conditioning (HVAC) system models are used for the purpose of control design, fault detection and diagnosis, system analysis, design and optimization. Therefore, ensuring the accuracy and reliability of the dynamic models is important before their application. Parameter tuning and model validation is a crucial way to improve the accuracy and reliability of the dynamic models. Traditional parameter tuning and validation methods are generally time-consuming, inaccurate and can only handle a limited number of tuning parameters. This is especially true for multiple-input-multiple-output (MIMO) models due to their intrinsic complexity. This dissertation proposes a new automatic parameter tuning and validation approach to address this problem. In this approach, a fast and accurate model is derived using linearization. Discrete-time convolution is then applied on this linearized model to generate the model outputs. These outputs and data are then processed through wavelet decomposition, and the corresponding wavelet coefficients obtained from it are used to establish the objective function. Wavelets are advantageous in capturing the dynamic information hidden in the time series. The objective function is then optimized iteratively using a hybrid method consisting of a global search genetic algorithm (GA) and a local gradient search method. In order to prove the feasibility and robustness of the proposed approach, it is applied on different dynamic models. These models include an HVAC system model with moving boundary (MB) heat exchanger models, a heat pump model with finite control volume (FCV) heat exchanger models, and a lumped parameter residential conditioned space model. These models generally have a large number of parameters which need tuning. The proposed method is proved to be efficient in tuning single data set, and can also tune the models using multiple experimental or field data sets with different operating conditions. The tuned parameters are further cross-validated using other data sets with different operating conditions. The results also indicate the proposed method can effectively tune the model using both static and transient data simultaneously

Fractional-Order PID Controllers for Temperature Control:A Review

Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications

A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors